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Probabilistic brain MR image transformation using generative models
Abstract Brain MR image transformation, which is the process of transforming MR images of one type to another, is critical to several downstream neuroimaging tasks that include brain tissue and lesion volume estimation and lesion detection. In recent years, several deep learning-based methods have been applied to address this task; however, for the most part, they have tended to be deterministic. These methods provide a single transformed output for a given input image with no accompanying measure of confidence in the transformation process. In contrast to this, in this study, we demonstrate how a class of probabilistic conditional generative algorithms can be applied to MR image transformation and quantify the performance of these algorithms. We also demonstrate that the ability to generate multiple transformed images for a given input image can be used to estimate the uncertainty in the output and to detect out-of-distribution (OOD) input images. In particular, we apply conditional Generative Adversarial Networks (cGAN), Noise Conditional Score Networks (NCSN), and Denoising Diffusion Probabilistic Models (DDPM) to transform T1, T2, FLAIR, and proton density (PD) MR images. Through extensive computational experiments, we conclude that the probabilistic algorithms are more accurate than other benchmark methods, and among these, the diffusion models yield the most accurate transformation results. Within the diffusion models, DDPM demonstrates higher performance in terms of similarity metrics, and NCSN exhibits accurate distributional measures and computationally favorable characteristics. We also demonstrate how the generative models can be used to assess the confidence in a given transformation and to detect input images that contain pathology and/or artifacts.
Assessment of equipment and human resources in a national survey of neonatal resuscitation in Iran
The association between APOA1 (rs5069) gene polymorphism and insulin resistance surrogates and metabolic indices among obese individuals with different glycemic statuses (euglycemic and T2DM)
Abstract Obesity significantly contributes to insulin resistance and type 2 mellitus diabetes (T2DM), with both environmental and genetic factors influencing metabolic risk. Apolipoprotein A1 (APOA1), a key regulator of lipid metabolism, has genetic variants such as rs5069 that may affect metabolic profiles. This study investigated the association between APOA1 (rs5069) polymorphism and metabolic risk among euglycemic and T2DM obese individuals compared to healthy controls. Three hundred participants were enrolled and divided into healthy controls, euglycemic obese, and T2DM obese groups. Demographic, biochemical, and metabolic parameters including fasting blood sugar (FBS), HbA1c, lipid profile, HOMA-IR, TyG index, TyG-BMI, and METS-IR were assessed. APOA1 (rs5069) genotyping was conducted. Statistical analyses included ANOVA, chi-square tests, and principal component analysis (PCA). Obese individuals, particularly those with T2DM, showed significantly elevated insulin resistance markers, dyslipidemia, and metabolic indices ( p < 0.001) compared to controls. The A allele of APOA1 (rs5069) was more frequent among obese participants. However, no significant differences in metabolic markers were observed among GG, GA, and AA genotypes within either obese group. PCA showed that metabolic variability was driven primarily by insulin resistance and lipid variables rather than genotype. While APOA1 (rs5069) genotype distribution varied across groups, it did not independently impact metabolic risk. Insulin resistance and dyslipidemia are the main contributors to metabolic disturbances in obesity, supporting the utility of non-invasive markers for early risk assessment.
Pharmacological evaluation as analgesic and anti-inflammatory and molecular docking of newly synthesized nitrogen heterocyclic derivatives
Abstract A new class of poly-fused pyrazolo, pyrano, and pyrimidino derivatives 2a , b-9a , b were synthesized in this study, and their biological properties as analgesics and anti-inflammatory agents were examined. The pharmacological activity of some synthesized substances were better than those of reference controls where, compounds 6b , 7b , 8b and 9b have strong analgesic activity in comparison while, compounds 7b , 8a and 9b have strong anti-inflammatory activity. Also, molecular docking, molecular dynamic (MD) simulations and thermodynamic calculation were studied. Detailed synthesis, pharmacological activity, spectroscopic analysis and molecular docking were provided.
Assessing production of 38K suitable for PET imaging via 38Ar(p, n)38K, 38Ar(d,2n)38K, and 36Ar(t, n)38K reactions using GEANT4, EMPIRE, and TALYS codes
Comparative tolerance and phytoremediation potential of four Lagerstroemia indica cultivars under cadmium stress
Research on intelligent pharmacy unmanned delivery vehicle path planning and drug recognition technology based on JPS and RRT algorithms
Present-day vertical land motions (VLM) of the Chesapeake Bay region derived from robust network imaging of global navigation satellite system (GNSS) observations
Analysis of ocular biometry in Korean using swept-source optical coherence tomography
Optimized intrusion detection for IoT networks using Cauchy–Gaussian hybrid evolutionary feature selection
Abstract The Internet of Things network is a prime target for attackers due to its vulnerabilities and the sensitive data it handles. Protecting these devices is critical, and an Intrusion Detection System serves as the first defence against breaches. While many intrusion classification methods exist, building low-complexity systems for IoT remains challenging. This paper introduces a novel method combining active feature selection and ensemble machine learning to address complexity issues for IDS in IoT. Specifically, a novel Cauchy–Gaussian genetic-arithmetic optimiser-driven variance-based active feature selection method is proposed. The proposed algorithm operates in two phases: in the first phase, the model learns active samples by representing them as a KD-tree based on feature variance; in the second phase, the Cauchy–Gaussian genetic-arithmetic optimiser uses the active samples to select relevant features. Cauchy and Gaussian distributions ensure diversity in population initialisation and prevent early convergence, enhancing population diversity in the initial phase. The proposed genetic arithmetic optimizer combines genetic and arithmetic operators; the optimizer balances exploration and exploitation, accelerating convergence while avoiding local minima. The proposed method is evaluated and validated on the CICIDS 2017 and IoTID20 datasets, demonstrating superior performance to conventional AOA and GA approaches. Moreover, active feature selection reduces the complexity of running wrapper methods by using active samples for feature selection. The proposed method achieved an accuracy of 99.88% and 99.72% with the Bagging algorithm, along with a low false positive rate of 0.000801 and 0.000165 with the CICIDS2017 and IoTID20 datasets.
Fertile high-K magmatism and hydrothermal alteration zone associated with porphyry copper mineralization in Samra area, SE Sinai, Egypt
Abstract This study combines remote sensing and geochemical data to evaluate the porphyry copper mineralization in the Samra region of southeastern Sinai, Egypt. The Wadi Samra area, located within the Kid metamorphic belt and Tarr Complex, comprises volcanic flows, pyroclastics, breccias, tuffs, mudstones, schists, and albitic intrusions. These rocks are intruded by high-K calc-alkaline granitoids. The study utilizes Landsat-8 spectral bands and ASTER data to analyze the distribution of ferrous and ferric iron oxides within copper belts. The Tarr Complex, located in the Wadi Samra area, is characterized by three distinct deformation phases, with thrust faults dipping toward the northwest controlling the contacts among rhyodacitic tuffs, pyroclastics, albitite, and porphyritic dacite. Porphyry copper mineralization in the Samra area of southeastern Sinai occurs within a volcano-sedimentary sequence intruded by high-K calc-alkaline granitoids. Mineralization styles include quartz veins, stockworks, disseminated sulfides, and alteration zones associated with primary (pyrite, chalcopyrite, bornite) and secondary (malachite, azurite) copper minerals. The granitoids linked to this mineralization—mainly quartz-diorite and granodiorite—are classified as I-type, magnetite-series rocks formed from hydrous magma at temperatures between 800 and 900 °C and 20–30 km depths. These geochemical and petrological characteristics suggest favorable conditions for porphyry copper with minor gold mineralizations.
Inspiratory muscle training and trunk control exercises on respiratory strength and motor function in spinal muscular atrophy: randomized controlled trial
HMLA: A hybrid machine learning approach for enhancing stroke prediction models with missing data imputation techniques
Abstract Early and accurate stroke prediction is critical to reduce death and disability risk, despite the presence of irrelevant and sparse information in clinical datasets that often undermines model performance. The novel machine learning approach is proposed for stroke prediction in the Cardiovascular Health Study (CHS) dataset. The proposed approach consists of two steps. The important features are selected using the Information Gain Ratio (IGR) during the preprocessing, and missing data handled by K-Nearest Neighbour (KNN), which also helps to enhance data integrity as well computing efficiency. Following the classification phase, a Deep Neural Network (DNN) model is trained on the preprocessed information to predict stroke risk. After classification, a DNN model is further trained using preprocessed data to predict the risk of stroke. Model assessment was based on a combined 10-fold nested cross-validation scheme for unbiased internal validation and to avoid data leakage. The effectiveness of the model was evaluated by seven statistical indices, false positive rate, precision, sensitivity, specificity, F1-score, accuracy and AUC-ROC comparison with classical classification methods. Although the developed framework at this study achieved an accuracy of 94.32%, precision of 95.96%, F1-score of 95.00%, specificity of 94.67% and sensitivity of 94.06%, the study is restricted to internal validation and a single optimizer (ALO), necessitating more assessment on external datasets. The findings indicate that the hybrid IGR–KNN–DNN framework offers strong predictive potential and computational efficiency for early stroke-risk assessment, with additional validation enhancing its clinical application.
A novel adaptive hybrid intrusion detection system with lightweight optimization for enhanced security in internet of medical things
Low-grade, systemic inflammation and the risk of perioperative neurocognitive disorders in an observational study of older adults
Abstract Aims The neuroinflammatory response to surgery may contribute to the pathogenesis of post-operative delirium (POD) and cognitive dysfunction (POCD), but whether inflammation before surgery enhances the risk of developing these conditions is unclear. Here, we investigate the relationship between preoperative levels of inflammation markers and the risk of POD/POCD. Methods 697 surgical patients aged ≥ 65 years were recruited 2014–2017 in Utrecht, the Netherlands and Berlin, Germany into the BioCog study. C-reactive protein (CRP), S100A12, interleukin-6 (IL-6) and IL-18 were measured immediately before surgery. POD was assessed twice daily up to 7 days/hospital discharge. POCD was determined from neuropsychological testing before surgery and 3 months thereafter. Multiple logistic regression analyses were run adjusted for age, sex, surgery site, and BMI. Results. 140 out of 697 patients (20.1%) developed POD during 7 days/by discharge; 50 out of 469 patients (10.9%) attending the 3-month follow-up developed POCD. CRP ≥ 10 mg/L was found in 149 patients and was not associated with POD/POCD. Among patients with CRP < 10 mg/L, higher S100A12 and higher CRP concentrations were each associated with higher POD risk (OR per SD higher concentrations, S100A12, 1.26, 95% CI 1.03, 1.54; CRP,1.42, 95% CI 1.15, 1.76). Higher S100A12 was associated with higher POCD risk (OR 1.40 per SD, 95% CI 1.04, 1.88). No associations were found for IL-6 and IL-18. Of note, only the result on CRP and POD survived Bonferroni correction with cut-off p < 0.006. Conclusion Low-grade inflammation may influence individual vulnerability to cognitive complications of surgery. Our results warrant further examination. Trial registration NCT02265263.
Integrated application of transcriptomics and metabolomics provides insights into gonadal differentiation in Mesocentrotus nudus
Abstract Mesocentrotus nudus is an important aquaculture species in East Asia, valued for its gonads as the only edible part. However, the molecular basis of gonadal differentiation in this species remains poorly understood. In this study, we determined that morphological gonadal differentiation occurs when individuals reach a test diameter of approximately 40 mm. Amino acid profiling revealed sex-specific differences between ovaries and testes, with higher levels of lysine, proline, alanine, and glutamic acid in testes, suggesting sexual dimorphism in metabolic demand. To investigate the regulatory mechanisms involved, we conducted integrated transcriptomic and metabolomic analyses between differentiated and undifferentiated gonads. Differentially expressed genes (DEGs) and differentially expressed metabolites (DEMs) including retinoic acid, linoleic acid, and arachidonic acid, were significantly enriched in retinol metabolism, steroid biosynthesis, and amino acid metabolic pathways. Several key genes, such as GATA4 , CYP17A1 , and HSD17B , were identified as potential markers for gonadal differentiation. Furthermore, components of the TGF-β signaling pathway ( Smads , Rbx1 , SKP ) and retinol metabolism genes ( CYP26 , CYP1A , CYP3A ) exhibited sex-biased expression patterns. This study provides novel insights into the molecular mechanisms underlying sex differentiation in sea urchins and lays a molecular foundation for the development of sex-control breeding strategies.